White Wine Exploratory Data Analysis by Irene Florez
Introduction
This project is a quick exploratory analysis project using R. The aim of the project is to use R to explore the relationships between data features.
We will be using a wine data set available at http://www3.dsi.uminho.pt/pcortez/wine/.
‘White Wine Quality’ is a tidy dataset which contains 4,898 white wines with 11 variables quantifying the chemical properties of each wine. At least 3 wine experts rated the quality of each wine, providing a rating between 0 (very bad) and 10 (very excellent).
We will use univariate, bivariate, and multivariate analyses to explore the relationships between the data features and to tease out the quality rating. You can read the final summary and reflection at the end of this document.
setup document
load packages
load the data
Univariate Plots Section
data variables
## [1] 4898 12## [1] "fixed.acidity" "volatile.acidity" "citric.acid"
## [4] "residual.sugar" "chlorides" "free.sulfur.dioxide"
## [7] "total.sulfur.dioxide" "density" "pH"
## [10] "sulphates" "alcohol" "quality"* there are 12 variables & a total of 4898 observations
data dimensions
## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.0 0.27 0.36 20.7 0.045
## 2 6.3 0.30 0.34 1.6 0.049
## 3 8.1 0.28 0.40 6.9 0.050
## 4 7.2 0.23 0.32 8.5 0.058
## 5 7.2 0.23 0.32 8.5 0.058
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 45 170 1.0010 3.00 0.45 8.8
## 2 14 132 0.9940 3.30 0.49 9.5
## 3 30 97 0.9951 3.26 0.44 10.1
## 4 47 186 0.9956 3.19 0.40 9.9
## 5 47 186 0.9956 3.19 0.40 9.9
## quality
## 1 6
## 2 6
## 3 6
## 4 6
## 5 6## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 3.800 Min. :0.0800 Min. :0.0000 Min. : 0.600
## 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700 1st Qu.: 1.700
## Median : 6.800 Median :0.2600 Median :0.3200 Median : 5.200
## Mean : 6.855 Mean :0.2782 Mean :0.3342 Mean : 6.391
## 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900 3rd Qu.: 9.900
## Max. :14.200 Max. :1.1000 Max. :1.6600 Max. :65.800
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.00900 Min. : 2.00 Min. : 9.0
## 1st Qu.:0.03600 1st Qu.: 23.00 1st Qu.:108.0
## Median :0.04300 Median : 34.00 Median :134.0
## Mean :0.04577 Mean : 35.31 Mean :138.4
## 3rd Qu.:0.05000 3rd Qu.: 46.00 3rd Qu.:167.0
## Max. :0.34600 Max. :289.00 Max. :440.0
## density pH sulphates alcohol
## Min. :0.9871 Min. :2.720 Min. :0.2200 Min. : 8.00
## 1st Qu.:0.9917 1st Qu.:3.090 1st Qu.:0.4100 1st Qu.: 9.50
## Median :0.9937 Median :3.180 Median :0.4700 Median :10.40
## Mean :0.9940 Mean :3.188 Mean :0.4898 Mean :10.51
## 3rd Qu.:0.9961 3rd Qu.:3.280 3rd Qu.:0.5500 3rd Qu.:11.40
## Max. :1.0390 Max. :3.820 Max. :1.0800 Max. :14.20
## quality
## Min. :3.000
## 1st Qu.:5.000
## Median :6.000
## Mean :5.878
## 3rd Qu.:6.000
## Max. :9.000## 'data.frame': 4898 obs. of 12 variables:
## $ fixed.acidity : num 7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
## $ volatile.acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
## $ citric.acid : num 0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
## $ residual.sugar : num 20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
## $ chlorides : num 0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
## $ free.sulfur.dioxide : num 45 14 30 47 47 30 30 45 14 28 ...
## $ total.sulfur.dioxide: num 170 132 97 186 186 97 136 170 132 129 ...
## $ density : num 1.001 0.994 0.995 0.996 0.996 ...
## $ pH : num 3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
## $ sulphates : num 0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
## $ alcohol : num 8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
## $ quality : int 6 6 6 6 6 6 6 6 6 6 ...* all of the data observations are num or int, there are not factor data types
features review: quality
## NULL## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.878 6.000 9.000* quality is a roughly normal bell shaped curve distribution
* quality values are distributed between 3-9
features review: alcohol
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.00 9.50 10.40 10.51 11.40 14.20
features review: sulphates
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2200 0.4100 0.4700 0.4898 0.5500 1.0800
Univariate Analysis
Bivariate Plots Section
alcohol & quality
##
## Pearson's product-moment correlation
##
## data: alcohol and quality
## t = 33.858, df = 4896, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4126015 0.4579941
## sample estimates:
## cor
## 0.4355747
sulphates & quality
alcohol & sulphates
##
## Pearson's product-moment correlation
##
## data: sulphates and alcohol
## t = 0.35834, df = 1559, p-value = 0.7201
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04055754 0.05866309
## sample estimates:
## cor
## 0.009075112
Multivariate Plots Section
## Ord.factor w/ 7 levels "3"<"4"<"5"<"6"<..: 4 4 4 4 4 4 4 4 4 4 ...
Final Plots and Summary
plot one
* Alchohol has a positive relationship (.43) with quality.
* Generally, higher quality wines will have higher alchohol content.
* Alchohol has a negative relationship with density.
plot two
* There’s a high concentration of 5-7 quality wines…
* and these largely have alchohol levels between 8.5-13.5
plot three
* Sulphates range from .2 to 1.2 and are mostly centered around .5
Reflection
EDA results: We reviewed three features in this data set: alcohol, quality, and sulphates. We found that alchohol has a positive relationship (.43) with quality. Generally, higher quality wines will have higher alchohol content. Sulphates and alchohol content generally do not have a strong relationship.
Process reflection: Here we used plotting (scatterplots, histograms, boxplots, and line graphs).
In the future, I would like to do a correlation matrix using all of the variables. I would also like to review ratios such as quality / alcohol. Secondly, I would like to do a deeper dive into alcohol and density. Lastly, I would like to create a model which with observations can predict wine quality with some degree of confidence. And overall, I would like more practice with transparency, jitter, smoothing, and limiting axes.